Effect of Fluid Contamination on Reverse Torque of Healing Abutments in Two Implant Connection Systems: In Vitro Study
Bibliographic record
Abstract
INTRODUCTION: This in vitro study investigates the impact of blood, saliva, and chlorhexidine contamination on the reverse torque values (RTVs) of healing abutments in two types of implant-abutment connections: internal hex and conical. METHODS: A total of 88 Ti6Al4V titanium alloy dental implants were tested, comprising two types of connections: internal hex (n = 44, MIS Seven) and conical (n = 44, MIS C1). Each group was further divided into four subgroups (n = 11) based on the type of contamination medium used. Healing abutments were tightened onto the implant fixtures using a torque of 25 Ncm with the designated medium, followed by retightening after a 10-min interval. After a two-week incubation period, reverse torque values (RTVs) were recorded using a digital torque gauge. To assess differences between the implant systems within the same medium, as well as differences between the medium types within the same implant system, the Welch two-sample t-test was employed. Additionally, linear regression analysis was conducted to evaluate the interaction between medium types and the implant systems. RESULTS: For MIS Seven implants, RTVs were consistently lower than the initial 25 Ncm torque across all contamination groups. In contrast, for MIS C1 implants, RTVs exceeded 25 Ncm in the saliva and blood contamination groups; however, the presence of chlorhexidine reduced the RTVs. A comparative analysis revealed that the C1 conical connection required significantly higher RTVs than the Seven internal hex system (coefficient = 3.318). No significant differences were observed between implant systems in the control and chlorhexidine groups. However, the C1 system required higher RTVs than the Seven system in the presence of saliva (p = 0.0006) and blood (p = 0.00009). Furthermore, the interaction analysis indicated that in the presence of saliva and blood contamination, C1 implants required significantly higher RTVs, with mean differences of 7.18 and 4.9 (Ncm), respectively, compared to the Seven implants. CONCLUSIONS: Conical implant-abutment connections generally require higher RTVs for abutment disconnection compared to internal hex connections. Both implant connection and the type of contaminant significantly affect RTVs. Chlorhexidine has been shown to reduce reverse torque for both types of connections. In contrast, contamination with saliva and blood tends to increase reverse torque, particularly for conical connections.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".